UVa 348 Optimal Array Multiplication Sequence (DP 最优矩阵链乘)

题意   求给定数目矩阵的最优链乘  并输出路径

矩阵链乘在小白P170有讲解 也是经典的动态规划 有了转移方程题目就好做了   设d[i][j]为矩阵i到矩阵j的最优链乘 初始状态d[i][i]=0 k为i,j之间的一个矩阵    x[i],y[i]分别表示第i个矩阵的行和列 则有d[i][j]=min{d[i][k]+d[k+1][j]+x[i]*y[k]*y[j]}
感觉这题比较难的就是路径打印了 仔细观察一下结构 其实也不难 记录每个k值 直接递归就行了

#include<cstdio>  
#include<cstring>  
using namespace std;  
#define maxn 11  
#define INF 0x3f3f3f3f  
#define t dp(i,k)+dp(k+1,j)+x[i]*y[k]*y[j]  
int d[maxn][maxn],pre[maxn][maxn],x[maxn],y[maxn],n;  
int dp(int i,int j)  
{  
    if(d[i][j]<INF) return d[i][j];  
    for(int k=i; k<j; ++k)  
        if(d[i][j]>t)  
        {  
            d[i][j]=t;  
            pre[i][j]=k;  
        }  
    return d[i][j];  
}  
  
void print(int i,int j)  
{  
    int k=pre[i][j];  
    if(k)  
    {  
        printf("(");  
        print(i,k);  
        printf(" x ");  
        print(k+1,j);  
        printf(")");  
    }  
    else  
    {  
        printf("A%d",i);  
    }  
}  
  
int main()  
{  
    int k=1;  
    while(scanf("%d",&n),n)  
    {  
        memset(d,0x3f,sizeof(d));  
        memset(pre,0,sizeof(pre));  
        for(int i=1; i<=n; ++i)  
        {  
            scanf("%d%d",&x[i],&y[i]);  
            d[i][i]=0;  
        }  
        dp(1,n);  
        printf("Case %d: ",k);  
        print(1,n);  
        printf("\n");  
        k++;  
    }  
    return 0;  
}  

 Optimal Array Multiplication Sequence 

Given two arrays A and B, we can determine the array C = A B using the standard definition of matrix multiplication:

The number of columns in the A array must be the same as the number of rows in the B array. Notationally, let's say that rows(A) and columns(A) are the number of rows and columns, respectively, in the A array. The number of individual multiplications required to compute the entire C array (which will have the same number of rows as A and the same number of columns as B) is then rows(A)columns(Bcolumns(A). For example, if A is a tex2html_wrap_inline67 array, and B is a tex2html_wrap_inline71 array, it will taketex2html_wrap_inline73 , or 3000 multiplications to compute the C array.

To perform multiplication of more than two arrays we have a choice of how to proceed. For example, if XY, and Z are arrays, then to compute X Y Z we could either compute (X YZ or X (Y Z). Suppose Xis a tex2html_wrap_inline103 array, Y is a tex2html_wrap_inline67 array, and Z is a tex2html_wrap_inline111 array. Let's look at the number of multiplications required to compute the product using the two different sequences:

(X YZ

  • tex2html_wrap_inline119 multiplications to determine the product (X Y), a tex2html_wrap_inline123 array.
  • Then tex2html_wrap_inline125 multiplications to determine the final result.
  • Total multiplications: 4500.

X (Y Z)

  • tex2html_wrap_inline133 multiplications to determine the product (Y Z), a tex2html_wrap_inline139 array.
  • Then tex2html_wrap_inline141 multiplications to determine the final result.
  • Total multiplications: 8750.

Clearly we'll be able to compute (X YZ using fewer individual multiplications.

Given the size of each array in a sequence of arrays to be multiplied, you are to determine an optimal computational sequence. Optimality, for this problem, is relative to the number of individual multiplications required.

Input

For each array in the multiple sequences of arrays to be multiplied you will be given only the dimensions of the array. Each sequence will consist of an integer N which indicates the number of arrays to be multiplied, and then N pairs of integers, each pair giving the number of rows and columns in an array; the order in which the dimensions are given is the same as the order in which the arrays are to be multiplied. A value of zero for N indicates the end of the input. N will be no larger than 10.

Output

Assume the arrays are named tex2html_wrap_inline157 . Your output for each input case is to be a line containing a parenthesized expression clearly indicating the order in which the arrays are to be multiplied. Prefix the output for each case with the case number (they are sequentially numbered, starting with 1). Your output should strongly resemble that shown in the samples shown below. If, by chance, there are multiple correct sequences, any of these will be accepted as a valid answer.

Sample Input

3
1 5
5 20
20 1
3
5 10
10 20
20 35
6
30 35
35 15
15 5
5 10
10 20
20 25
0

Sample Output

Case 1: (A1 x (A2 x A3))
Case 2: ((A1 x A2) x A3)
Case 3: ((A1 x (A2 x A3)) x ((A4 x A5) x A6))



### 矩阵链乘法问题概述 矩阵链乘法问题是动态规划中的经典问题之一,其目标是最小化计算多个矩阵连乘所需的标量乘法次数。给定一组矩阵 \( \langle A_1, A_2, ..., A_n \rangle \),其中每个矩阵 \( A_i \) 的维度为 \( p_{i-1} \times p_i \)[^4],我们需要找到一种最优的括号化方案来最小化总的运算成本。 --- ### 动态规划的核心思想 动态规划通过将复杂问题分解成子问题并存储中间结果的方式减少重复计算。具体到矩阵链乘法问题上: 1. **定义状态变量** 定义 \( m[i][j] \) 表示从第 \( i \) 个矩阵到第 \( j \) 个矩阵之间的最小子问题解决方案的成本[^1]。 2. **递推关系** 对于任意分割点 \( k \) (\( i \leq k < j \)),有以下递推公式: \[ m[i][j] = \min\limits_{i \leq k < j}(m[i][k] + m[k+1][j] + p_{i-1}p_kp_j) \] 其中,最后一项表示两个子链相乘时的额外开销[^3]。 3. **边界条件** 当只有一个矩阵时(即 \( i = j \)),无需任何操作,因此 \( m[i][i] = 0 \)[^2]。 4. **构建解路径** 使用辅助数组 \( s[i][j] \) 来记录每次划分的最佳位置 \( k \),以便最终重建完整的括号表达式[^5]。 --- ### 动态规划算法的具体实现 以下是基于上述理论的一种 Python 实现方式: ```python def matrix_chain_order(p): n = len(p) - 1 # 初始化二维表 m 和 s m = [[0]*n for _ in range(n)] s = [[0]*n for _ in range(n)] # 自底向上填充表格 for l in range(2, n+1): # 子链长度 for i in range(n-l+2): j = i + l - 1 if j >= n: continue m[i][j] = float('inf') for k in range(i, j): q = m[i][k] + m[k+1][j] + p[i-1]*p[k]*p[j] if q < m[i][j]: m[i][j] = q s[i][j] = k return m, s def print_optimal_parens(s, i, j): if i == j: print(f"A{i}", end="") else: print("(", end="") print_optimal_parens(s, i, s[i][j]) print_optimal_parens(s, s[i][j]+1, j) print(")", end="") # 测试数据 dimensions = [30, 35, 15, 5, 10, 20, 25] # 计算最优顺序及其代价 cost_table, split_points = matrix_chain_order(dimensions) print("\nOptimal Parenthesization:") print_optimal_parens(split_points, 0, len(dimensions)-2) print("\nMinimum cost:", cost_table[0][-1]) ``` #### 输出解释 该程序会打印出最佳括号化的形式以及对应的最低运算成本。例如输入 `dimensions=[30, 35, 15, 5, 10, 20, 25]` 可能得到如下输出: ``` Optimal Parenthesization: ((A0(A1A2))((A3A4)A5)) Minimum cost: 15125 ``` --- ### 复杂度分析 时间复杂度主要由三重嵌套循环决定,总共有大约 \( O(n^3) \) 次迭代。空间复杂度则取决于用于保存子问题解的二维数组大小,约为 \( O(n^2) \)。 --- ###
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